---
title: "Get Started"
id: "get-started"
description: "Learn how to get up and running with Haystack. The page contains instructions for installing, running your first RAG pipeline, adding data and further resources."
---
# Build your first RAG application

Let's build your first Retrieval Augmented Generation (RAG) pipeline and see how Haystack answers questions.

First, install the minimal form of Haystack:

```shell
pip install haystack-ai
```

<details>

<summary>Are you already using Haystack 1.x?</summary>

> 🚧 Warning
>
> Installing `farm-haystack` and `haystack-ai` in the same Python environment (virtualenv, Colab, or system) causes problems.
>
> Installing both packages in the same environment can somehow work or fail in obscure ways. We suggest installing only one of these packages per Python environment. Make sure that you remove both packages if they are installed in the same environment, followed by installing only one of them:
>
> ```bash
> pip uninstall -y farm-haystack haystack-ai
> pip install haystack-ai
> ```
>
> If you have any questions, please reach out to us on the [GitHub Discussion](https://github.com/deepset-ai/haystack/discussions) or [Discord](https://discord.com/invite/VBpFzsgRVF).

</details>

In the example below, we show how to set an API key using a Haystack [Secret](/docs/intro). However, for easier use, you can also set an OpenAI key as an `OPENAI_API_KEY` environment variable.

```python
from haystack import Pipeline, Document
from haystack.utils import Secret
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
from haystack.components.generators import OpenAIGenerator
from haystack.components.builders.prompt_builder import PromptBuilder

# Write documents to InMemoryDocumentStore
document_store = InMemoryDocumentStore()
document_store.write_documents([
    Document(content="My name is Jean and I live in Paris."),
    Document(content="My name is Mark and I live in Berlin."),
    Document(content="My name is Giorgio and I live in Rome.")
])

# Build a RAG pipeline
prompt_template = """
Given these documents, answer the question.
Documents:
{% for doc in documents %}
    {{ doc.content }}
{% endfor %}
Question: {{question}}
Answer:
"""

retriever = InMemoryBM25Retriever(document_store=document_store)
prompt_builder = PromptBuilder(template=prompt_template)
llm = OpenAIGenerator(api_key=Secret.from_token(api_key))

rag_pipeline = Pipeline()
rag_pipeline.add_component("retriever", retriever)
rag_pipeline.add_component("prompt_builder", prompt_builder)
rag_pipeline.add_component("llm", llm)
rag_pipeline.connect("retriever", "prompt_builder.documents")
rag_pipeline.connect("prompt_builder", "llm")

# Ask a question
question = "Who lives in Paris?"
results = rag_pipeline.run(
    {
        "retriever": {"query": question},
        "prompt_builder": {"question": question},
    }
)

print(results["llm"]["replies"])
```

## Adding Your Data

Instead of running the RAG pipeline on example data, learn how you can add your own custom data using [Document Stores](/docs/intro).
